关于比较双因子和二次因子模型的说明:贝叶斯信息标准是模型选择的常规可靠指数吗?
Tenko Raykov1, Christine DiStefano2, Lisa Calvocoressi3
1Michigan State University, East Lansing, USA.
Educational and psychological measurement
|June 20, 2024
概括
贝叶斯信息标准 (BIC) 可能无法在双因子和二次因子模型之间进行可靠的选择. 研究人员应该保持谨慎,因为BIC可能会错误地偏好二阶模型,即使数据符合双因素模型.
科学领域:
- 心理测量 心理测量 心理测量
- 统计建模 统计建模
背景情况:
- 贝叶斯信息标准 (BIC) 常用于统计分析中的模型选择.
- 双因子和二次因子模型经常用于解释多维数据的结构.
研究的目的:
- 评估BIC在区分双因子模型和二次因子模型方面的可靠性.
- 调查基于BIC的模型选择中的潜在差异,当数据从双因素结构生成时.
主要方法:
- 在各种样本大小中使用双因素模型模拟数据生成.
- 模型合适指数的比较,特别是BIC,对于双因素模型和二次因素模型.
主要成果:
- 两因素模型始终被认为低于基于BIC值的二级模型.
- 尽管数据是从多次复制的双因素模型中生成的,但这种情况发生了.
结论:
- 在双因素和二级模型之间选择模型时,常规依赖BIC可能会产生误导性.
- 研究人员应该意识到BIC在这些特定情况下准确识别数据生成模型方面的局限性.
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